open-compress/claw-compactor

• highreadme#1

  Reposition README's core identity statement to include key differentiators

  原因：

  当前

  Claw Compactor is an open-source **LLM token compression engine** built around a 14-stage **Fusion Pipeline**.

  复制粘贴的修复

  复制

  Claw Compactor is an open-source **14-stage Fusion Pipeline for LLM token compression** — offering reversible compression, AST-aware code analysis, and intelligent content routing with zero LLM inference cost.

  待办完成忽略
• mediumreadme#2

  Add a 'Comparison' section to differentiate from related tools

  原因：

  复制粘贴的修复

  复制

  ## How is Claw Compactor Different?
  Unlike LLM quantization techniques (e.g., GPTQ, AWQ, bitsandbytes) that reduce model size or inference speed, Claw Compactor focuses on **reducing the input token count** to the LLM, achieving zero inference cost for the compression itself. While it utilizes AST-aware analysis similar to parsing libraries (e.g., Tree-sitter, ANTLR), its primary function is **reversible token compression** through a multi-stage pipeline, not just parsing.

  待办完成忽略
• lowreadme#3

  Add explicit 'Use Cases' to clarify target problems

  原因：

  复制粘贴的修复

  复制

  ## Use Cases
  -   **Reducing LLM API Costs:** Significantly lower token usage for prompts and responses.
  -   **Expanding Context Windows:** Fit more information into an LLM's context without increasing token count.
  -   **Optimizing RAG Pipelines:** Compress retrieved documents before feeding them to an LLM.
  -   **Code Analysis & Summarization:** Leverage AST-aware compression for efficient processing of source code.

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  How to reduce LLM inference costs and optimize context window usage?

  你：未被推荐
  AI 推荐顺序：

  1. GPTQ
  2. AWQ
  3. bitsandbytes
  4. Medusa
  5. Google's Speculative Decoding
  6. FlashAttention-2
  7. xFormers
  8. vLLM
  9. Text Generation Inference (TGI)
  10. LlamaIndex
  11. LangChain
  12. LLMLingua
  13. LongLLMLingua
  14. NVIDIA TensorRT-LLM
  15. Hugging Face Transformers library

  AI 推荐了 15 个替代方案，却始终没点名 open-compress/claw-compactor。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  Seeking a tool for reversible LLM prompt compression using AST-aware code analysis.

  你：未被推荐
  AI 推荐顺序：

  1. Tree-sitter
  2. ANTLR
  3. Esprima
  4. ESTree-walker
  5. Babel's `@babel/traverse`
  6. escodegen
  7. `ast` module
  8. `astor` library
  9. `unparse` module
  10. Roslyn

  AI 推荐了 10 个替代方案，却始终没点名 open-compress/claw-compactor。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  pass

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of open-compress/claw-compactor?

  pass
  ✓AI 明确点名了 open-compress/claw-compactor

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts open-compress/claw-compactor in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 open-compress/claw-compactor

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo open-compress/claw-compactor solve, and who is the primary audience?

  pass
  ⚠AI 未点名 open-compress/claw-compactor —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？